homology modeling, molecular docking and virtual screening...

Biomedical Letters 2020; 6(1):17-22

138

Homology modeling, molecular docking and virtual screening to reveal potential inhibitor of ALS associated protein guanine nucleotide exchange C9ORF72 Syeda Maheen Abid1*, Muhammad Ali2 1Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan 2 District Head Quarter Hospital, Chiniot, Pakistan

Abstract Repeated expansion of hexanucleotide in C9ORF72 encodes the protein

Guanine Nucleotide Exchange considered as the main cause of Amyotrophic

Lateral Sclerosis (ALS). The repeated expansion produces toxic products and

autophagy deficits. Various in silico approaches were employed for structural

3D modeling and protein-protein molecular docking analyses of C9ORF72

followed by virtual screening. Homology modeling and threading approaches

were applied to predict the 3D structures of C9ORF72 and 92.38% of quality

factor was calculated by ERRAT evaluation tool. STRING database was

utilized, and it was observed that SMCR8 has the ability to be the interacting

partner of C9ORF72. Protein-protein molecular docking analyses of C9ORF72

with SMCR8 were performed and potential interacting residues were observed

computationally. FDA library from ZINC database was utilized for virtual

screening and comparative molecular docking analyses were performed by

AutoDock Vina. It was proposed that the scrutinized compound ZINC131 have

strong binding affinities and least binding energy of -11.3 kcal/mol. The

suggested molecule may be used for further analyses in the drug discovery

processes. The predicted 3D structure of C9ORF72 provides the structural

insights for the better functional understanding of C9ORF72. Overall, the

findings of present work may be helpful in designing the novel therapeutic

targets against C9ORF72.

A R T I C L E I N F O

Received

March 23, 2020

Revised

May 07, 2020

Accepted

June 15, 2020

*Corresponding Author

Syeda Maheen Abid

E-mail

[email protected]

Keywords

Molecular docking

ALS

C9ORF72

Homology modeling

Virtual screening

AutoDock Vina

How to Cite

Abid SM, Ali M. Homology

modeling, molecular docking and

virtual screening to reveal

potential inhibitor of ALS

associated protein guanine

nucleotide exchange C9ORF72.

Biomedical Letters 2020;

6(2):138-148.

Open Access

This work is licensed under the Creative Commons Attribution Non-

Commercial 4.0 International License.

Special Issue: Computational drug designing and molecular docking analyses

Research article 2020 | Volume 6 | Issue 2 | Pages 138-148

Biomedical Letters ISSN 2410-955X– An International Biannually Journal

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139

Introduction

Amyotrophic Lateral Sclerosis (ALS) was first

described by Charcot in 1874 [1]. The cause of

ALS/Lou Gehrig disease is not completely known and

considered as spontaneously arise [2]. In ALS, upper

motor neurons and lower motor neurons degenerate

and die (motor neurons link between the brain and

voluntary muscles) [3]. ALS affects the nerve cells in

the brain and spinal cord. The brain lost the ability to

initiate and control the movement of muscles [4]. The

gradual decline in strength leads to paralysis of more

and more muscles leads to death. The genetic cause of

ALS includes mutation in C9ORF72, FUS, SOD1,

VCP and TDP-43 [5]. Most of the cases for ALS are

sporadic and about 25% of people were suffering for

ALS due to family history [6]. The sporadic ALS are

usually to the patients between the age of 55-65 years

old and only 5% patients are <30 years old. Males and

females are equally affected by ALS. Juvenile ALS

(JALS) is a term used for patients below the age of 25

years [7].

Most of the time ALS is autosomal recessive while

dominant inheritance linked with chromosome 9 [8].

ALS is a motor neuron disease characterized by

muscle twitching [9], muscle stiffness and muscle

weakness due to a decrease in muscle size. In most

cases, patient lose the ability to speak, walk, breathe,

swallow and hand movement [10, 11]. Some patients

face difficulties in thinking and behavioral acts [12].

ALS has no cure yet however early diagnoses may

help to treat and keep the muscle control little longer

[13]. The death of ALS patient cause within 3 to 4

years due to respiratory failure [14]. ALS is related to

parkinsonism and dementia [15]. There is no specific

cure of ALS, moreover Riluzole have considerable

relax to the patients [16]. Another drug approved by

FDA for ALS is Edaravone [17].

C9ORF72 is localized on chromosome 9 [18].

Augmentation of GGGGCC hexanucleotide repeat

extensively present in C9ORF72 considered as the

common cause of ALS [19]. A repetition of

hexanucleotides is considered as the genetic cause of

almost 10% patients [20]. Guanine nucleotide

exchange C9ORF72 is encoded by C9ORF72 [21].

The structure of C9ORF72 is not known yet.

C9ORF72 is present on the short arm of the

chromosome (p) in humans [22]. The length of the

sequence is from 27,546,542 base pairs to 27,573,863

base pairs. The protein guanine nucleotide exchange

C9ORF72 is present in most of the regions of the brain, presynaptic terminals and in the cytoplasm of

neurons [23, 24]. In C9ORF72, there are fewer repeats

of hexanucleotide GGGGCC as <30 normally

however in patients of ALS, these repeats are in

hundreds [25, 26]. These repeats decrease the

autophagy regulator of protein C9ORF72 alters the

expression leads to ALS. The lack of C9ORF72 might

be the cause of disease. C9ORF72 emerged in most of

the eukaryotes and have single copy of gene encoding

C9ORF72 [27]. The presence of four nucleotides of

Guanine and two of Cytosine noncoding part

repetition cause severe kind of mutational changes

leads to ALS [28, 29]. In C9ORF72 hexanucleotide

repetition can cause RNA toxicity through the

confiscate and collection of RNA binding protein. The

guanine nucleotide exchange protein has two

isoforms; one has the sequence length of 481 amino

acids while the other has 222 amino acids. Repeated

expansion cause mutation in C9ORF72 leads to ALS.

The neuronal function of C9ORF72 is unknown.

C9ORF72 has structural homology with Differentially

Expressed in Normal Neoplasia DENN [30].

During the last two decades, the number of known

protein sequences has increased as compared to

structures [31]. This unbalance between the protein

sequence and structure has censoriously limited the

ability to understand the molecular mechanism of

proteins [32]. The structure formation rate of known

protein is much slower as the structure prediction

techniques (X-Ray Crystallography and NMR) are

time consuming [33]. The development of structural

bioinformatics helps to solve the biological

macromolecules (DNA, RNA and protein) structural

analyses [34]. There have been many achievements in

computational drug designing and personalized

medicine [35, 36]. Various possibilities are present to

understand neurological disease which plays an

important role in the medical field [37-39]. The focus

of current work was to 3D structure prediction,

evaluation and validation of Guanine Nucleotide

Exchange C9ORF72 followed by protein-protein

molecular docking and virtual screening.

Materials and Methods

The C9ORF72 have accession number Q96LT7 in

Uniport Knowledge Base. In this work, 3D structure

prediction, virtual screening and molecular docking

analyses were performed.

The amino acid sequence (FASTA sequences) of

Guanine nucleotide exchange C9ORF72 was

retrieved from Uniport KB (http://www.uniprot.org/)

[40]. The sequence was subjected to BLASTp for the

selection of a suitable template against Protein Data

Bank (PDB) [41, 42]. The automated program


140

MODELLER 9.20 [43] for homology modeling was

used to predict the 3D structures of C9ORF72 by

spatial restraints. The online tools including I-

TASSER [44], RaptorX [45], CPHModel [46],

HHpred [47], Phyre2 [48], SWISS-MODEL [49],

MOD-WEB [50], Robetta [51], Sparks-X [52], 3D-

Jigsaw [53] and ESyPred 3D [54] were also used to

predict the protein structure. The 3D structures of

C9ORF72 was visualized by UCSF Chimera 1.13.1

[55] and PyMOL [56]. UCSF Chimera also used to

minimize selected structure. Rampage [57], Anolea

[58], ProCheck [59] and ERRAT [60] evaluations

tools were used to evaluate the quality of the model of

protein structure. The produced Ramachandran plots

for the assessment of predicted structures showed

residues dispensation and also declared ϕ and ψ

distribution of non-proline and non-glycine residues.

For the differentiation of favorable and non-favorable

regions, phi and psi angles were plotted against each

other. These angles were used for the assessment of

different regions. ERRAT evaluation tool was also

used to calculate the quality factors of predicted

structures [61].

To determine the functional interacting partner of

target protein, STRING (Search tool for the retrieval

of interacting genes/proteins) [62] and STITCH

(Search Tool Interacting Chemical) [63] were used.

The online server PatchDock [64, 65] was used for

protein-protein molecular docking and FireDock [66]

was used for the refinement and scoring of protein-

protein docking solutions. Gramm-X was also

employed for protein-protein docking analysis for the

cross validation of the analyses. LigPlot [67, 68] was

utilized to analyze the hydrophobic and electrostatic

interaction and also used to generate schematic

diagrams of protein-protein interactions.

PyRx [69] software was used to dock the small

molecules with macromolecule and virtual screening.

The blind docking was proceeded to analyze the

protein and ligand interaction for confirmation and

orientation. The FDA library of Zinc database [70]

was retrieved for virtual screening against the target

protein [71].

Results and Discussion

The study of neurology and structural bioinformatics

are fields of exploring knowledge and providing an

effective way for better understanding and

development of different research approaches for the

diagnosis, cure and detection of neuronal diseases

including ALS. The ensemble genome browser was

used to locate the exact position of C9ORF72 protein-

coding gene in humans (Fig. 1).

Fig. 1: The presence of gene C9orf72 on the position of chromosome number 8.

It has been observed from extensive literature review

that Guanine Nucleotide Exchange protein has no

other reported member in family. The multiple

sequence alignment (MSA) was performed for two

isoforms of C9ORF72 carried out by Clustal omega

[72]. An asteric(*) indicated positions which have

single, fully conserved residues, colon indicated the

similar residues and dot indicated the weakly similar

residues. The positions with no dot indicated non-

conserved residues (Fig. 2).

Coils, Protparam [73] tools were used to calculate the

physiochemical properties of the receptor protein. The

molecular weight of the protein based on average

isotope masses of the amino acids was also studied.

The theoretical PI was 5.82 depends on side chains

determined the pH of the protein. The half-life of the

protein was calculated 30 hours in vitro. The aliphatic

index was -0.069, instability index was 50.54 and the number of positively charged residues were 50 as well

as negatively charged residues with total number of an

atom was also calculated 60 (Table 1) (Fig. 3).

The PONDR [74] tool was used to predict the ratio of

natural disorders caused by the mutation in C9ORF72.

The graph showed the composition of order and

disorder. The center line was the threshold and peak

above the threshold identified as disorder while the

line below the threshold identified as an order of given

protein C9ORF72 (Fig. 4).

Structure Prediction

The 3D structure of C9ORF72 was not reported by X-

Ray crystallography and NMR yet. The comparative

modeling and threading approaches


141

Fig. 2: Alignment retrieved from the Clustal Omega of the related protein of mouse and bovine with a human which

shows residues with * (identical) and: (somewhat similar).

Fig. 3: Pie chart representation of amino acid composition of C9ORF72 and calculated percentage values.


142

Fig. 4: Disorder residues of Guanine Nucleotide exchange C9ORF72.

were employed to predict the 3D structures. The

BLASTp was used for the submission of protein

sequence against PDB for the retrieval of suitable

templates. Only one template has been appeared

against the query sequence (Table 2).

Table 1: Calculated features of C9ORF72.

Features Calculations

Aliphatic index -0.069

Instability index 50.54

Total number of positive charge residues

(Arg + Lys)

50

Total number of negative charge residues

(Asp + Glu)

60

Total number of atoms 7683

All the 50 structures were evaluated on the bases of

favored region, quality factor, allowed region and

outlier regions. A comparative graph has been plotted

to analyze the suitable structure among all the

predicted structures. The suitable structure was

selected from the plotted graph (Fig. 5).

There was variation in the quality factor values of the

predicted structures and the selected structure of

Guanine nucleotide exchange C9ORF72 has the

overall quality factor of 92.38%. The Ramachandran

plot was employed to evaluate the quality of the

predicted structures and the selected structure has

99.60% value of the favored region, 0.40% of allowed

region. Interestingly, no amino acid was

observed in the outlier region. The energy

minimization of the selected structure was performed

to improve the stereochemical properties of the

predicted structure. The minimization was performed

by UCSF Chimera 1.13 with 1000 steepest decent and

1000 conjugate gradient runs (Fig. 6).

Protein-protein molecular docking analyses

Guanine Nucleotide Exchange expressed in many

parts of the body specifically expressed in the brain.

SMCR8, the interacting partner of C9ORF72 was

analyzed and observed by employing STRING and

STITCH databases for protein-protein molecular

docking analyses. Comparative molecular docking

analyses were done to evaluate the binding residues.

The docked complexes of SMCR8 and C9ORF72

analyses predicted the interacting residues and

analyzed on the basis of ACE [75] by utilizing

PatchDock (Table 2). Numerous docked complexes

were generated and then top 10 complexes with least

ACE values were selected for further refinement

through FireDock. The docked complexes were

evaluated on the basis of least binding global energy.

The molecular docking analyses suggested that

SMCR8 and C9ORF72 have effective binding affinity

[76]. The interacting residues were analyzed through

UCSF Chimera (Table 3) (Fig. 7).

Table 2: BlastP against PDB. Description Max score Total score Query Coverage E-Value Identity Accession

Cap-Associated protein CAF20 27.3 27.3 4% 7.84 5.83% 6FC3


143

Fig. 5: Graph of quality factor, favored region, allowed region and outliers region of the C90RF72 structure prediction

analyses evaluated through different software.

Fig. 6: 3D structure of ALS associated protein Guanine Nucleotide Exchange C9ORF72.


144

Table 3: Protein-protein interacting residues.

Interacting Protein Interacting protein residues Targeting Protein Targeting protein residues

SMCR8

LYS 15, GLN 21, TYR 24,

GLN17, LEU 18, ASN 20,

ALA 19, ARG 43, ASN 23,

TYR 39, LYS 42, GLU 36,

GLY 35, SER 34, ARG 33,

LYS 5, TYR 54

Guanine

Nucleotide

Exchange

C9ORF72

PRO 467, THR 470, TYR 469, GLY

465, PHE 462, PHE 464, SER 361,

VAL 384, PRO 389, LEU 402, SER

395, ALA 399, LEU 403, ARG 407,

GLN 400, SER 392, ARG 329, TYR

326, LEU 391

Molecular docking analysis

The FDA library of ZINC databases was screened by

using AutoDock Vina. After screening the FDA

library of ZINC database, top ranked 4 compounds

were observed. Comparative molecular docking

analyses were done by utilizing AutoDock Vina (Fig.

7).

Fig. 7: Protein-Protein docking analysis and the interacting residues.

The generated docked complexes were ranked on the

bases of least binding energy. The screened

compounds showed similar binding domain. The

selected compound may have potential against

C9ORF72. The variation was observed in analyzed

complexes having least binding energy. The

compound ZINC131 showed least binding energy of -

11.3 kcal/mol and 2D structure of the least binding

affinity were develop from the ChemDraw Ultra 8.0. A plot of ligand-protein interactions was analyzed by

employing UCSF Chimera (Fig. 8).


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Fig. 8: The interactions of top ranked compound with C9ORF72. The residues analyzed from AutoDock Vina and

UCSF Chimera.

Fig. 9: 2D structure of least binding affinity compound from the molecular docking.

The function of protein depends on protein structures.

The structural bioinformatics opens the way towards

more progress in the analyses of protein function. The

computational method of structure prediction is less

time consuming [77]. The era of computational

biology which is necessary for the prediction of

function contributes well in the way of research.

Conclusion

By employing computational approaches and in silico

analyses, the analyzed molecules showed binding

residues in conserved region by AutoDock Vina. The

in silico molecular docking analyses proposed that

binding residues are significant to control the

expression of C9ORF72. The observed results

suggested that the selected molecule could be used for

novel chemical compounds.

Acknowledgments

Authors are thankful to the Department of

Bioinformatics and Biotechnology, Government

College University Faisalabad to provide the research

platform.


146

Conflict of interest

The authors declare no conflicts of interest.

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